Sepsis has evolved as an enormous health issue amongst critically ill patients. It is a major risk factor that results in multiple organ failure and shock. Acute kidney injury (AKI) is one of the most frequent complications underlying sepsis, which portends a heavy burden of mortality and morbidity. Thus, the present review is aimed to provide an insight into the recent progression in the molecular mechanisms targeting dysregulated immune response and cellular dysfunction involved in the development of sepsis-associated AKI, accentuating the phytoconstituents as eligible candidates for attenuating the onset and progression of sepsis-associated AKI. The pathogenesis of sepsis-mediated AKI entails a complicated mechanism and is likely to involve a distinct constellation of hemodynamic, inflammatory, and immune mechanisms. Novel biomarkers like neutrophil gelatinase-associated lipocalin, soluble triggering receptor expressed on myeloid cells 1, procalcitonin, alpha-1-microglobulin, and presepsin can help in a more sensitive diagnosis of sepsis-associated AKI. Many bioactive compounds like curcumin, resveratrol, baicalin, quercetin, and polydatin are reported to play an important role in the prevention and management of sepsis-associated AKI by decreasing serum creatinine, blood urea nitrogen, cystatin C, lipid peroxidation, oxidative stress, IL-1β, TNF-α, NF-κB, and increasing the activity of antioxidant enzymes and level of PPARγ. The plant bioactive compounds could be developed into a drug-developing candidate in managing sepsis-mediated acute kidney injury after detailed follow-up studies. Lastly, the gut-kidney axis may be a more promising therapeutic target against the onset of septic AKI, but a deeper understanding of the molecular pathways is still required. 

Sepsis-mediated renal dysfunction: Pathophysiology, biomarkers and role of phytoconstituents in its management

Sepsis-associated kidney injury is common in critically ill patients and significantly increases morbidity and mortality rates. Several complex pathophysiological factors contribute to its presentation and perpetuation, including macrocirculatory and microcirculatory changes, mitochondrial dysfunction, and metabolic reprogramming. Recovery from acute kidney injury (AKI) relies on the evolution towards adaptive mechanisms such as endothelial repair and tubular cell regeneration, while maladaptive repair increases the risk of progression to chronic kidney disease. Fundamental management strategies include early sepsis recognition and prompt treatment, through the administration of adequate antimicrobial agents, fluid resuscitation, and vasoactive agents as needed. In septic patients, organ-specific support is often required, particularly renal replacement therapy (RRT) in the setting of severe AKI, although ongoing debates persist regarding the ideal timing of initiation and dosing of RRT. A comprehensive approach integrating early recognition, targeted interventions, and close monitoring is essential to mitigate the burden of SA-AKI and improve patient outcomes in critical care settings. 

Acute Kidney Injury in Sepsis

Background and objective Acute Kidney Injury (AKI) is a common and severe complication in patients diagnosed with sepsis. It is associated with higher mortality rates, prolonged hospital stays, increased utilization of medical resources, and financial burden on patients’ families. This study aimed to establish and validate predictive models using machine learning algorithms to accurately predict the occurrence of AKI in patients diagnosed with sepsis. Methods This retrospective study utilized real observational data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. It included patients aged 18 to 90 years diagnosed with sepsis who were admitted to the ICU for the first time and had hospital stays exceeding 48 hours. Predictive models, employing various machine learning algorithms including Light Gradient Boosting Machine (LightGBM), EXtreme Gradient Boosting (XGBoost), Random Forest (RF), Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression (LR), were developed. The dataset was randomly divided into training and test sets at a ratio of 4:1. Results A total of 10,575 sepsis patients were included in the analysis, of whom 8,575 (81.1%) developed AKI during hospitalization. A selection of 47 variables was utilized for model construction. The models derived from LightGBM, XGBoost, RF, DT, ANN, SVM, and LR achieved AUCs of 0.801, 0.773, 0.772, 0.737, 0.720, 0.765, and 0.776, respectively. Among these models, LightGBM demonstrated the most superior predictive performance. Conclusions These machine learning models offer valuable predictive capabilities for identifying AKI in patients diagnosed with sepsis. The LightGBM model, with its superior predictive capability, could aid clinicians in early identification of high-risk patients.

Machine learning for prediction of acute kidney injury in patients diagnosed with sepsis in critical care

Septic acute kidney injury (SAKI) is one of the most common and life-threatening complications of sepsis. Patients with SAKI have increased mortality. However, the underlying pathogenesis is unclear, and the treatment targeting SAKI is unsatisfactory. Thus, identifying optimal biomarkers for SAKI diagnosis and treatment is an urgent requisite. Accumulating evidence indicates that noncoding RNAs (ncRNAs) are involved in the occurrence and progression of SAKI. In the present review, we summarized the studies of ncRNAs in SAKI, including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs). The ncRNAs are divided into protective and damage factors according to their role in SAKI, and their expression patterns, functions, and molecular mechanisms were elaborated. Next, we proposed that ncRNAs have the potential to be diagnostic and prognostic biomarkers for SAKI and as new therapeutic targets. This review aimed to provide a comprehensive overview of ncRNAs in SKAI and explored the clinical value of ncRNAs as ideal biomarkers of SAKI. 

Roles of noncoding RNAs in septic acute kidney injury.

Abstract Background Sepsis is perceived as lethal tissue damage and significantly increases mortality in combination with acute kidney injury (AKI). M2 macrophages play important roles in the secretion of anti‐inflammatory and tissue repair mediators. We aimed to study the role of Dehydroandrographolide (Deh) in sepsis‐associated AKI in vitro and in vivo through lipopolysaccharide (LPS)‐induced macrophages model and cecal ligation and puncture‐induced AKI mice model, and to reveal the mechanism related to M2 macrophage polarization. Methods Enzyme‐linked immunosorbent assay kits were used to assess the levels of inflammatory factors. Expression of markers related to M1 macrophages and M2 macrophages were analyzed. Additionally, dual specificity phosphatase 3 (DUSP3) expression was tested. Cell apoptosis was evaluated by flow cytometry analysis and terminal‐deoxynucleotidyl transferase‐mediated nick end labeling staining. Moreover, renal histological assessment was performed by using hematoxylin and eosin staining. Results Deh reduced inflammation of THP‐1‐derived macrophages exposed to LPS. Besides, Deh induced the polarization of M1 macrophages to M2 and downregulated DUSP3 expression in THP‐1‐derived macrophages under LPS conditions. Further, DUSP3 overexpression reversed the impacts of Deh on the inflammation and M2 macrophages polarization of THP‐1‐derived macrophages stimulated by LPS. Additionally, human proximal tubular epithelial cells (HK‐2) in the condition medium from DUSP3‐overexpressed THP‐1‐derived macrophages treated with LPS and Deh displayed decreased viability and increased apoptosis and inflammation. The in vivo results suggested that Deh improved the renal function, ameliorated pathological injury, induced the polarization of M1 macrophages to M2, suppressed inflammation and apoptosis, and downregulated DUSP3 expression in sepsis‐induced mice. Conclusion Deh facilitated M2 macrophage polarization by downregulating DUSP3 to inhibit septic AKI.

Dehydroandrographolide facilitates M2 macrophage polarization by downregulating DUSP3 to inhibit sepsis‐associated acute kidney injury

Sepsis acute kidney injury (SAKI) is a common complication of sepsis, accounting for 26-50 % of all acute kidney injury (AKI). AKI is an independent risk factor for increased mortality risk in patients with sepsis. The excessive inflammatory cascade reaction in SAKI is one of the main causes of kidney damage. Both the innate immune system and the adaptive immune system are involved in the inflammation process of SAKI. Under the action of endotoxin, neutrophils, monocytes, macrophages, T cells and other complex immune network reactions occur, and a large number of endogenous inflammatory mediators are released, resulting in the amplification and loss of control of the inflammatory response. The study of immune cells in SAKI will help improve the understanding of the immune mechanisms of SAKI, and will lay a foundation for the development of new diagnostic and therapeutic targets. This article reviews the role of known immune mechanisms in the occurrence and development of SAKI, with a view to finding new targets for SAKI treatment. 

Immunoregulatory mechanism of acute kidney injury in sepsis: A Narrative Review.

Objective: To investigate the efficacy and safety of dexmedetomidine (DEX) for postoperative patient controlled intravenous analgesia (PCIA). Measurements: Two investigators independently searched Pubmed, Embase, Scopus, Cochrane Library and CBM for randomized controlled trials of DEX for PCIA. Main results: Thirty-seven studies with a total of 5,409 patients were included in this meta-analysis. Compared with analgesics alone, DEX for PCIA reduced pain score at 24 h [mean difference (MD) = −0.70; 95% confidence interval (CI): −0.85, −0.54; p < 0.00001, I 2 = 90%] and 48 h postoperatively (MD = −0.43; 95% CI: −0.52, −0.34; p < 0.00001, I 2 = 96%). Moreover, DEX reduced analgesics consumption during the first 24 h [standardized mean difference (SMD) = −0.25; 95% CI: −0.34, −0.16; p < 0.00001, I 2 = 91%] and the number of resuscitation analgesics administered [odds ratio (OR) = 0.54; 95% CI: 0.44, 0.66; p < 0.00001, I 2 = 72%]. Furthermore, DEX improved patient satisfaction (OR = 3.55; 95% CI: 2.36, 5.35; p < 0.00001, I 2 = 60%), and reduced incidence of side effects, such as postoperative nausea and vomiting (PONV) (OR = 0.47; 95% CI: 0.39, 0.57; p < 0.00001, I 2 = 59%) and pruritus after surgery (OR = 0.45; 95% CI: 0.30, 0.68; p = 0.0001, I 2 = 0%). Besides, DEX attenuates inflammatory cytokine levels, such as IL-6 (MD = −5.73; 95% CI: −8.34, −3.12; p < 0.00001, I 2 = 91%) and TNF-α (MD = −0.63; 95% CI: −0.76, −0.50; p < 0.00001, I 2 = 89%). Finally, DEX increased the risk of bradycardia (OR = 1.66; 95% CI: 1.12, 2.45; p = 0.01, I 2 = 15%), but the complication of hypotension did not differ between the two groups (OR = 1.30; 95% CI: 0.84, 2.04; p = 0.25, I 2 = 0%). Conclusion: DEX is used for postoperative PCIA analgesia, which can significantly improve the analgesic effect, effectively control postoperative inflammatory response, reduce the dosage and adverse reactions of analgesics, and improve postoperative patient satisfaction. Of course, the impact of the immunosuppressive effect of DEX on the prognosis of patients needs further study. Systematic review registration: CRD42022340933, https://www.crd.york.ac.uk/prospero/.

Efficacy and safety evaluation of dexmedetomidine for postoperative patient controlled intravenous analgesia: A systematic review and meta-analysis

This study used a metabolomic approach to reveal changes in the levels of metabolic biomarkers and related metabolic pathways before and after Zhuang Yao Shuang Lu Tong Nao granule (YHT) treatment in rats with cerebral ischemia. The neurological deficit scores were significantly higher in the MCAO_R group than in the NC group, indicating that the mice had significantly impaired motor functions. The YHT group had significantly lower scores than the MCAO_R group, suggesting that YHT significantly improved motor function in rats. TTC staining of the brain tissue revealed that YHT significantly reduced the area of cerebral infarction in the treated rats. The MCAO_R group was better separated from the NC rent, sham, and YHT groups via metabolomic PCA. Moreover, there were significant differences in the differential metabolites between the MACO_R and YHT groups. Eighteen common differential metabolites were detected between the MACO_R and NC groups, MACO_R and sham groups, and MACO_R and YHT groups, indicating that YHT significantly increased the levels of various metabolites in the serum of cerebral ischemic stroke (CIS) rats. Moreover, a total of 23 metabolic pathways were obtained. We identified 11 metabolic pathways with the most significant effects in the bubble plots. In conclusion, from a systems biology perspective, this metabolomics-based study showed that YHT could be used to treat ischemic stroke by modulating changes in endogenous metabolites.

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Metabolomics-Based Pharmacodynamic Analysis of Zhuang Yao Shuang Lu Tong Nao Granules in a Rat Model of Ischemic Cerebral Infarction

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Immunoregulatory mechanism of acute kidney injury in sepsis: A Narrative Review.

Efficacy and safety evaluation of dexmedetomidine for postoperative patient controlled intravenous analgesia: A systematic review and meta-analysis

Metabolomics-Based Pharmacodynamic Analysis of Zhuang Yao Shuang Lu Tong Nao Granules in a Rat Model of Ischemic Cerebral Infarction